In the training stage, the output of RNN layer will be passed to the integration layer for calculating the training loss. In the reference stage, the RNN layer computes the learned IMU measurements, which will be directly used as the input for the probabilistic sensor fusion.
We note that, our network architecture is relatively preliminary, which can be further largely optimized via a number of methods (e.g., using [1]}).
However, our results show that the current design already outperforms competing methods significantly, which strongly support our design purpose of using DNNs to process IMU data in inertial navigation.
